ArticleNeuroImage. Clinical2022
NigraNet: An automatic framework to assess nigral neuromelanin content in early Parkinson's disease using convolutional neural network.
Article in NeuroImage. Clinical, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 19 citations in OpenAlex.
- Volumetric and Intrastructural Characterization of the Substantia Nigra Using Optimized Synthetic MR Images Derived From Quantitative T1 Maps at 3 T.NMR in biomedicine · 2026Article
- Nigrostriatal imaging in patients with clinically uncertain Parkinsonian syndromes: a head-to-head comparison between SPECT, PET, and MRI.European journal of nuclear medicine and molecular imaging · 2026Article
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Candidate Right Locus Coeruleus-Centered Symptom Pathways in Young Major Depressive Disorder: A Multimodal Magnetic Resonance Imaging Study.Human brain mapping · 2026Article
- Pharmacologically Modulating the Noradrenergic Arousal System to Reduce Freezing of Gait in Parkinson's Disease: Study Protocol of the International AnTi-FREEZE Study.The European journal of neuroscience · 2026Article
- Fully automated segmentation of substantia nigra toward longitudinal analysis of Parkinson's disease.International journal of computer assisted radiology and surgery · 2026Article
- Parkinson's disease progression is shaped by longitudinal changes in cerebral compensation.Brain : a journal of neurology · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Harnessing routine MRI for the early screening of Parkinson's disease: a multicenter machine learning study using T2-weighted FLAIR imaging.Insights into imaging · 2025Article
- Deep-learning segmentation of the substantia nigra from multiparametric MRI: Application to Parkinson's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Early brain iron changes in Parkinson's disease and isolated rapid eye movement sleep behaviour disorder: a four-year longitudinal multimodal quantitative MRI study.Brain communications · 2025Article
- Neuromelanin-sensitive MRI for mechanistic research and biomarker development in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Regional nigral neuromelanin degeneration in asymptomatic leucine-rich repeat kinase 2 gene carrier using MRI.Scientific reports · 2024Article
- Classification of Parkinson's disease by deep learning on midbrain MRI.Frontiers in aging neuroscience · 2024Article
- An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.Human brain mapping · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) demonstrates neurodegenerative changes in the substantia nigra pars compacta (SNc) using neuromelanin-sensitive (NM)-MRI. As SNc manual segmentation is prone to substantial inter-individual variability across raters, development of a robust automatic segmentation framework is necessary to facilitate nigral neuromelanin quantification. Artificial intelligence (AI) is gaining traction in the neuroimaging community for automated brain region segmentation tasks using MRI.
objectiveDeveloping and validating AI-based NigraNet, a fully automatic SNc segmentation framework allowing nigral neuromelanin quantification in patients with PD using NM-MRI.
methodsWe prospectively included 199 participants comprising 144 early-stage idiopathic PD patients (disease duration = 1.5 ± 1.0 years) and 55 healthy volunteers (HV) scanned using a 3 Tesla MRI including whole brain T1-weighted anatomical imaging and NM-MRI. The regions of interest (ROI) were delineated in all participants automatically using NigraNet, a modified U-net, and compared to manual segmentations performed by two experienced raters. The SNc volumes (Vol), volumes corrected by total intracranial volume (C
resultsUsing both methods, Vol, C
conclusionsWe presented an AI-based NigraNet framework that utilizes a small MRI training dataset to fully automatize the SNc segmentation procedure with an increased precision and more reproducible results. Considering the consistency, accuracy and speed of our approach, this study could be a crucial step towards the implementation of a time-saving non-rater dependent fully automatic method for studying neuromelanin changes in clinical settings and large-scale neuroimaging studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.